Various objects such as semiconductor wafers, printed circuit boards, solar panels and microelectromechanical (MEMS) devices are manufactured by manufacturing processes that are highly complex and expensive.
Manufacturing process errors may result in yield limiting defects. The manufacturing is assisted by a Yield Management System (YMS). The YMS collects and analyzes manufacturing and test data coming from various tools at various manufacturing stages. The YMS is aimed at quickly identify tools and processes that impact yield.
Defect detection is usually performed by applying an inspection process that is followed by a review process. The inspection process may be performed by an optical inspection tool or by an electron beam inspection tool, and is aimed to find suspected defects. The review process is usually executed by a scanning electron microscope (SEM) and is aimed to determine which suspected defects are actual defects and if so—to which class (type) of defects these actual defects belong to. The review process includes acquiring SEM images of suspected defects and processing the SEM images by a classifier.
Typically, the inspection tools and the review tools are connected via a fab communication system and the inspection-review flow is controlled by the YMS. For example, the YMS assign a lot of semiconductor wafers to a certain inspection tool (for either an optical or ebeam inspection). The results of inspection—list of wafer locations representing possible defects and certain inspection attributes associated with these locations—are provided to the YMS. The YMS, in a manual, semi-automated for fully automated manner, identifies locations of interest. For example, the inspection tool and/or the YMS may identify a certain result as a ‘nuisance’ or a ‘true defect’; The YMS typically selects a subset of the ‘true defect’ locations and sends the locations of interest to an assigned review tool. The review tool reviews the locations of interest and their vicinity and generates additional data e.g. SEM images, respective image processing attributes, defects class (type), and returns data to the YMS. The number of suspected defects per object, as provided by today's inspection systems, may exceed one million. The review process (especially the acquisition of the SEM images) is relatively long. Imaging each one of the suspected defects will result in a review process that will be unreasonably long. Thus, many techniques are known in the art for improving inspection throughput, review throughput, quality of inspection results, quality of review results, YMS operation.
Furthermore, current inspection systems employ a fixed nuisance filter that outputs a limited number of suspected defects per wafer. The fixed nuisance filter is being setup during the recipe setup process and therefore cannot properly track production changes that occur in the manufacturing process and/or in the inspection process over time. For each suspected defect a number of attributes are calculated. The low resolution of the inspection system, relative to SEM, may provide inadequate information about the suspected defects.
Accordingly, there is a growing need to provide a higher sensitivity results out of wafer inspection combined with more effective and accurate filtering mechanism that will maintain the true defect of interest while filtering the nuisance. There is a growing need to automate systems and processes for measurement optimization. There is a growing demand for improved data interpretation for management of semiconductor fabrication yield.